Different materials and objects reflect and emit different wavelengths of electromagnetic radiation. Hyperspectral imaging involves collecting images of objects within a scene at multiple wavelengths of the electromagnetic radiation using a sensor. The spectrum of radiation captured at each pixel of the sensor can then be analysed to provide information about the makeup of the objects observed by the pixel.
Hyperspectral imaging techniques facilitate the locating and identifying of objects within the scene with high accuracy, provided prior spectral information of the objects is available. If no prior knowledge is available, then the technique is limited to the locating and identifying of objects which are highly anomalous with the scene background.
The performance of hyperspectral methods is dependent on the extent and accuracy of the predetermined spectral information. However, atmospheric conditions for example, can attenuate and otherwise degrade the typical spectrum reflected off objects within the scene, which degrades the signal that can be observed by an imaging system. This reduces the ability of the hyperspectral technique to discriminate one object from another.
Several different atmospheric correction techniques have been proposed, but these techniques can be slow and require large amounts of data for an accurate correction to be applied.
In addition to the above problems, detection algorithms which process the hyperspectral image data require statistics about the data in order to improve the detection of objects. The calculation of the statistics can be slow and require large amounts of data for accurate calculation. These issues impede the use of high fidelity hyperspectral techniques for high speed or real time applications.